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Task058 have RuntimeError: Given transposed=1, weight of size [128, 64, 1, 2, 2], expected input[2, 64, 16, 160, 160] to have 128 channels, but got 64 channels instead #65
Comments
hey,bro, I have the same problem. Have you solved it? |
I have the same problem |
I found a possible solution to this, The possible solution I did was to use a custom planner derived from Please let me know if you guys found a better solution. |
I also have the same problem @MenxLi would you mind giving a more detailed explanation of how you fixed it? I am trying to follow your steps but I get the following error "RuntimeError: Could not find the Planner class MyExperimentPlanner_v21. Make sure it is located somewhere in nnunet.experiment_planning". I have made my custom planner like so:
This I have then saved in the experiment_planning folder. If you can help me I would greatly appreciate your help! |
In the end I have solved the issue by using smaller resolution images. Using smaller images the algorithm will determine to use fewer pooling layers and thus the error goes away. |
It's great to hear that it works, as long as you can have fewer pooling layers, it will work. |
Yes you are right I probably haven't installed it with the -e option, I didn't even know about it ahah :) |
could you please tell me how to derive a custom planner from ExperimentPlanner3D_v2, I need help! I would appreciate it if you could tell me |
I try to train Electron Microscopy dataset by use Task058_ISBI_EM_SEG.py, but occur RuntimeError: Given transposed=1, weight of size [128, 64, 1, 2, 2], expected input[2, 64, 16, 160, 160] to have 128 channels, but got 64 channels instead
UnetPlusPlus code has anly update ? Or need to modify something?
Thank you very mach!
My command :
python3.7 Task058_ISBI_EM_SEG.py
nnUNet_plan_and_preprocess -t 058 --verify_dataset_integrity
nnUNet_train 3d_fullres nnUNetPlusPlusTrainerV2 Task058_ISBI_EM_SEG 0 --npz
print logs:
###############################################
I am running the following nnUNet: 3d_fullres
My trainer class is: <class 'nnunet.training.network_training.nnUNetPlusPlusTrainerV2.nnUNetPlusPlusTrainerV2'>
For that I will be using the following configuration:
num_classes: 1
modalities: {0: 'EM'}
use_mask_for_norm OrderedDict([(0, False)])
keep_only_largest_region None
min_region_size_per_class None
min_size_per_class None
normalization_schemes OrderedDict([(0, 'nonCT')])
stages...
stage: 0
{'batch_size': 2, 'num_pool_per_axis': [2, 6, 6], 'patch_size': array([ 16, 320, 320]), 'median_patient_size_in_voxels': array([ 30, 512, 512]), 'current_spacing': array([50., 4., 4.]), 'original_spacing': array([50., 4., 4.]), 'do_dummy_2D_data_aug': True, 'pool_op_kernel_sizes': [[1, 2, 2], [1, 2, 2], [1, 2, 2], [2, 2, 2], [2, 2, 2], [1, 2, 2]], 'conv_kernel_sizes': [[1, 3, 3], [1, 3, 3], [1, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3], [3, 3, 3]]}
I am using stage 0 from these plans
I am using sample dice + CE loss
I am using data from this folder: /UnetPlusPlus/UNetPlusPlus-master/UNetPlusPlus-master/pytorch/dataset/nnUNet_preprocessed/Task058_ISBI_EM_SEG/nnUNetData_plans_v2.1
###############################################
14:21:02.764737: Using dummy2d data augmentation
[[1, 2, 2], [1, 2, 2], [1, 2, 2], [2, 2, 2], [2, 2, 2], [1, 2, 2]]
loading dataset
loading all case properties
unpacking dataset
done
6
6
256
2
<class 'nnunet.network_architecture.generic_UNetPlusPlus.ConvDropoutNormNonlin'>
<class 'torch.nn.modules.conv.ConvTranspose3d'>
6
128
2
<class 'nnunet.network_architecture.generic_UNetPlusPlus.ConvDropoutNormNonlin'>
<class 'torch.nn.modules.conv.ConvTranspose3d'>
6
4
5
weight_decay: 3e-05
14:21:06.468369: lr: 0.01
using pin_memory on device 0
using pin_memory on device 0
14:21:08.702853: Unable to plot network architecture:
14:21:08.703407: No module named 'hiddenlayer'
14:21:08.703701:
printing the network instead:
14:21:08.703849: Generic_UNetPlusPlus(
(loc0): ModuleList(
(0): Sequential(
(0): StackedConvLayers(
(blocks): Sequential(
(0): ConvDropoutNormNonlin(
(conv): Conv3d(640, 320, kernel_size=[3, 3, 3], stride=(1, 1, 1), padding=[1, 1, 1])
(instnorm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)
(lrelu): LeakyReLU(negative_slope=0.01, inplace=True)
)
)
)
(1): StackedConvLayers(
(blocks): Sequential(
(0): ConvDropoutNormNonlin(
(conv): Conv3d(320, 320, kernel_size=[3, 3, 3], stride=(1, 1, 1), padding=[1, 1, 1])
(instnorm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)
(lrelu): LeakyReLU(negative_slope=0.01, inplace=True)
)
)
)
)
(1): Sequential(
(0): StackedConvLayers(
(blocks): Sequential(
(0): ConvDropoutNormNonlin(
(conv): Conv3d(960, 320, kernel_size=[3, 3, 3], stride=(1, 1, 1), padding=[1, 1, 1])
(instnorm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)
(lrelu): LeakyReLU(negative_slope=0.01, inplace=True)
)
)
)
(1): StackedConvLayers(
(blocks): Sequential(
(0): ConvDropoutNormNonlin(
(conv): Conv3d(320, 320, kernel_size=[3, 3, 3], stride=(1, 1, 1), padding=[1, 1, 1])
(instnorm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)
(lrelu): LeakyReLU(negative_slope=0.01, inplace=True)
)
)
)
)
(2): Sequential(
(0): StackedConvLayers(
(blocks): Sequential(
(0): ConvDropoutNormNonlin(
(conv): Conv3d(1024, 256, kernel_size=[3, 3, 3], stride=(1, 1, 1), padding=[1, 1, 1])
(instnorm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)
(lrelu): LeakyReLU(negative_slope=0.01, inplace=True)
)
)
)
(1): StackedConvLayers(
(blocks): Sequential(
(0): ConvDropoutNormNonlin(
(conv): Conv3d(256, 256, kernel_size=[3, 3, 3], stride=(1, 1, 1), padding=[1, 1, 1])
(instnorm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)
(lrelu): LeakyReLU(negative_slope=0.01, inplace=True)
)
)
)
)
(3): Sequential(
(0): StackedConvLayers(
(blocks): Sequential(
(0): ConvDropoutNormNonlin(
(conv): Conv3d(640, 128, kernel_size=[3, 3, 3], stride=(1, 1, 1), padding=[1, 1, 1])
(instnorm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)
(lrelu): LeakyReLU(negative_slope=0.01, inplace=True)
)
)
)
(1): StackedConvLayers(
(blocks): Sequential(
(0): ConvDropoutNormNonlin(
(conv): Conv3d(128, 128, kernel_size=[3, 3, 3], stride=(1, 1, 1), padding=[1, 1, 1])
(instnorm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)
(lrelu): LeakyReLU(negative_slope=0.01, inplace=True)
)
)
)
)
(4): Sequential(
(0): StackedConvLayers(
(blocks): Sequential(
(0): ConvDropoutNormNonlin(
(conv): Conv3d(384, 64, kernel_size=[1, 3, 3], stride=(1, 1, 1), padding=[0, 1, 1])
(instnorm): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)
(lrelu): LeakyReLU(negative_slope=0.01, inplace=True)
)
)
)
(1): StackedConvLayers(
(blocks): Sequential(
(0): ConvDropoutNormNonlin(
(conv): Conv3d(64, 64, kernel_size=[1, 3, 3], stride=(1, 1, 1), padding=[0, 1, 1])
(instnorm): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)
(lrelu): LeakyReLU(negative_slope=0.01, inplace=True)
)
)
)
)
(5): Sequential(
(0): StackedConvLayers(
(blocks): Sequential(
(0): ConvDropoutNormNonlin(
(conv): Conv3d(224, 32, kernel_size=[1, 3, 3], stride=(1, 1, 1), padding=[0, 1, 1])
(instnorm): InstanceNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)
(lrelu): LeakyReLU(negative_slope=0.01, inplace=True)
)
)
)
(1): StackedConvLayers(
(blocks): Sequential(
(0): ConvDropoutNormNonlin(
(conv): Conv3d(32, 32, kernel_size=[1, 3, 3], stride=(1, 1, 1), padding=[0, 1, 1])
(instnorm): InstanceNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)
(lrelu): LeakyReLU(negative_slope=0.01, inplace=True)
)
)
)
)
)
(loc1): ModuleList(
(0): Sequential(
(0): StackedConvLayers(
(blocks): Sequential(
(0): ConvDropoutNormNonlin(
(conv): Conv3d(640, 320, kernel_size=[3, 3, 3], stride=(1, 1, 1), padding=[1, 1, 1])
(instnorm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)
(lrelu): LeakyReLU(negative_slope=0.01, inplace=True)
)
)
)
(1): StackedConvLayers(
(blocks): Sequential(
(0): ConvDropoutNormNonlin(
(conv): Conv3d(320, 320, kernel_size=[3, 3, 3], stride=(1, 1, 1), padding=[1, 1, 1])
(instnorm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)
(lrelu): LeakyReLU(negative_slope=0.01, inplace=True)
)
)
)
)
(1): Sequential(
(0): StackedConvLayers(
(blocks): Sequential(
(0): ConvDropoutNormNonlin(
(conv): Conv3d(768, 256, kernel_size=[3, 3, 3], stride=(1, 1, 1), padding=[1, 1, 1])
(instnorm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)
(lrelu): LeakyReLU(negative_slope=0.01, inplace=True)
)
)
)
(1): StackedConvLayers(
(blocks): Sequential(
(0): ConvDropoutNormNonlin(
(conv): Conv3d(256, 256, kernel_size=[3, 3, 3], stride=(1, 1, 1), padding=[1, 1, 1])
(instnorm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)
(lrelu): LeakyReLU(negative_slope=0.01, inplace=True)
)
)
)
)
(2): Sequential(
(0): StackedConvLayers(
(blocks): Sequential(
(0): ConvDropoutNormNonlin(
(conv): Conv3d(512, 128, kernel_size=[3, 3, 3], stride=(1, 1, 1), padding=[1, 1, 1])
(instnorm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)
(lrelu): LeakyReLU(negative_slope=0.01, inplace=True)
)
)
)
(1): StackedConvLayers(
(blocks): Sequential(
(0): ConvDropoutNormNonlin(
(conv): Conv3d(128, 128, kernel_size=[3, 3, 3], stride=(1, 1, 1), padding=[1, 1, 1])
(instnorm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)
(lrelu): LeakyReLU(negative_slope=0.01, inplace=True)
)
)
)
)
(3): Sequential(
(0): StackedConvLayers(
(blocks): Sequential(
(0): ConvDropoutNormNonlin(
(conv): Conv3d(320, 64, kernel_size=[1, 3, 3], stride=(1, 1, 1), padding=[0, 1, 1])
(instnorm): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)
(lrelu): LeakyReLU(negative_slope=0.01, inplace=True)
)
)
)
(1): StackedConvLayers(
(blocks): Sequential(
(0): ConvDropoutNormNonlin(
(conv): Conv3d(64, 64, kernel_size=[1, 3, 3], stride=(1, 1, 1), padding=[0, 1, 1])
(instnorm): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)
(lrelu): LeakyReLU(negative_slope=0.01, inplace=True)
)
)
)
)
(4): Sequential(
(0): StackedConvLayers(
(blocks): Sequential(
(0): ConvDropoutNormNonlin(
(conv): Conv3d(192, 32, kernel_size=[1, 3, 3], stride=(1, 1, 1), padding=[0, 1, 1])
(instnorm): InstanceNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)
(lrelu): LeakyReLU(negative_slope=0.01, inplace=True)
)
)
)
(1): StackedConvLayers(
(blocks): Sequential(
(0): ConvDropoutNormNonlin(
(conv): Conv3d(32, 32, kernel_size=[1, 3, 3], stride=(1, 1, 1), padding=[0, 1, 1])
(instnorm): InstanceNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)
(lrelu): LeakyReLU(negative_slope=0.01, inplace=True)
)
)
)
)
)
(loc2): ModuleList(
(0): Sequential(
(0): StackedConvLayers(
(blocks): Sequential(
(0): ConvDropoutNormNonlin(
(conv): Conv3d(512, 256, kernel_size=[3, 3, 3], stride=(1, 1, 1), padding=[1, 1, 1])
(instnorm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)
(lrelu): LeakyReLU(negative_slope=0.01, inplace=True)
)
)
)
(1): StackedConvLayers(
(blocks): Sequential(
(0): ConvDropoutNormNonlin(
(conv): Conv3d(256, 256, kernel_size=[3, 3, 3], stride=(1, 1, 1), padding=[1, 1, 1])
(instnorm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)
(lrelu): LeakyReLU(negative_slope=0.01, inplace=True)
)
)
)
)
(1): Sequential(
(0): StackedConvLayers(
(blocks): Sequential(
(0): ConvDropoutNormNonlin(
(conv): Conv3d(384, 128, kernel_size=[3, 3, 3], stride=(1, 1, 1), padding=[1, 1, 1])
(instnorm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)
(lrelu): LeakyReLU(negative_slope=0.01, inplace=True)
)
)
)
(1): StackedConvLayers(
(blocks): Sequential(
(0): ConvDropoutNormNonlin(
(conv): Conv3d(128, 128, kernel_size=[3, 3, 3], stride=(1, 1, 1), padding=[1, 1, 1])
(instnorm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)
(lrelu): LeakyReLU(negative_slope=0.01, inplace=True)
)
)
)
)
(2): Sequential(
(0): StackedConvLayers(
(blocks): Sequential(
(0): ConvDropoutNormNonlin(
(conv): Conv3d(256, 64, kernel_size=[1, 3, 3], stride=(1, 1, 1), padding=[0, 1, 1])
(instnorm): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)
(lrelu): LeakyReLU(negative_slope=0.01, inplace=True)
)
)
)
(1): StackedConvLayers(
(blocks): Sequential(
(0): ConvDropoutNormNonlin(
(conv): Conv3d(64, 64, kernel_size=[1, 3, 3], stride=(1, 1, 1), padding=[0, 1, 1])
(instnorm): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)
(lrelu): LeakyReLU(negative_slope=0.01, inplace=True)
)
)
)
)
(3): Sequential(
(0): StackedConvLayers(
(blocks): Sequential(
(0): ConvDropoutNormNonlin(
(conv): Conv3d(160, 32, kernel_size=[1, 3, 3], stride=(1, 1, 1), padding=[0, 1, 1])
(instnorm): InstanceNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)
(lrelu): LeakyReLU(negative_slope=0.01, inplace=True)
)
)
)
(1): StackedConvLayers(
(blocks): Sequential(
(0): ConvDropoutNormNonlin(
(conv): Conv3d(32, 32, kernel_size=[1, 3, 3], stride=(1, 1, 1), padding=[0, 1, 1])
(instnorm): InstanceNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)
(lrelu): LeakyReLU(negative_slope=0.01, inplace=True)
)
)
)
)
)
(loc3): ModuleList(
(0): Sequential(
(0): StackedConvLayers(
(blocks): Sequential(
(0): ConvDropoutNormNonlin(
(conv): Conv3d(256, 128, kernel_size=[3, 3, 3], stride=(1, 1, 1), padding=[1, 1, 1])
(instnorm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)
(lrelu): LeakyReLU(negative_slope=0.01, inplace=True)
)
)
)
(1): StackedConvLayers(
(blocks): Sequential(
(0): ConvDropoutNormNonlin(
(conv): Conv3d(128, 128, kernel_size=[3, 3, 3], stride=(1, 1, 1), padding=[1, 1, 1])
(instnorm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)
(lrelu): LeakyReLU(negative_slope=0.01, inplace=True)
)
)
)
)
(1): Sequential(
(0): StackedConvLayers(
(blocks): Sequential(
(0): ConvDropoutNormNonlin(
(conv): Conv3d(192, 64, kernel_size=[1, 3, 3], stride=(1, 1, 1), padding=[0, 1, 1])
(instnorm): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)
(lrelu): LeakyReLU(negative_slope=0.01, inplace=True)
)
)
)
(1): StackedConvLayers(
(blocks): Sequential(
(0): ConvDropoutNormNonlin(
(conv): Conv3d(64, 64, kernel_size=[1, 3, 3], stride=(1, 1, 1), padding=[0, 1, 1])
(instnorm): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)
(lrelu): LeakyReLU(negative_slope=0.01, inplace=True)
)
)
)
)
(2): Sequential(
(0): StackedConvLayers(
(blocks): Sequential(
(0): ConvDropoutNormNonlin(
(conv): Conv3d(128, 32, kernel_size=[1, 3, 3], stride=(1, 1, 1), padding=[0, 1, 1])
(instnorm): InstanceNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)
(lrelu): LeakyReLU(negative_slope=0.01, inplace=True)
)
)
)
(1): StackedConvLayers(
(blocks): Sequential(
(0): ConvDropoutNormNonlin(
(conv): Conv3d(32, 32, kernel_size=[1, 3, 3], stride=(1, 1, 1), padding=[0, 1, 1])
(instnorm): InstanceNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)
(lrelu): LeakyReLU(negative_slope=0.01, inplace=True)
)
)
)
)
)
(loc4): ModuleList(
(0): Sequential(
(0): StackedConvLayers(
(blocks): Sequential(
(0): ConvDropoutNormNonlin(
(conv): Conv3d(128, 64, kernel_size=[1, 3, 3], stride=(1, 1, 1), padding=[0, 1, 1])
(instnorm): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)
(lrelu): LeakyReLU(negative_slope=0.01, inplace=True)
)
)
)
(1): StackedConvLayers(
(blocks): Sequential(
(0): ConvDropoutNormNonlin(
(conv): Conv3d(64, 64, kernel_size=[1, 3, 3], stride=(1, 1, 1), padding=[0, 1, 1])
(instnorm): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)
(lrelu): LeakyReLU(negative_slope=0.01, inplace=True)
)
)
)
)
(1): Sequential(
(0): StackedConvLayers(
(blocks): Sequential(
(0): ConvDropoutNormNonlin(
(conv): Conv3d(96, 32, kernel_size=[1, 3, 3], stride=(1, 1, 1), padding=[0, 1, 1])
(instnorm): InstanceNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)
(lrelu): LeakyReLU(negative_slope=0.01, inplace=True)
)
)
)
(1): StackedConvLayers(
(blocks): Sequential(
(0): ConvDropoutNormNonlin(
(conv): Conv3d(32, 32, kernel_size=[1, 3, 3], stride=(1, 1, 1), padding=[0, 1, 1])
(instnorm): InstanceNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)
(lrelu): LeakyReLU(negative_slope=0.01, inplace=True)
)
)
)
)
)
(conv_blocks_context): ModuleList(
(0): StackedConvLayers(
(blocks): Sequential(
(0): ConvDropoutNormNonlin(
(conv): Conv3d(1, 32, kernel_size=[1, 3, 3], stride=(1, 1, 1), padding=[0, 1, 1])
(instnorm): InstanceNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)
(lrelu): LeakyReLU(negative_slope=0.01, inplace=True)
)
(1): ConvDropoutNormNonlin(
(conv): Conv3d(32, 32, kernel_size=[1, 3, 3], stride=(1, 1, 1), padding=[0, 1, 1])
(instnorm): InstanceNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)
(lrelu): LeakyReLU(negative_slope=0.01, inplace=True)
)
)
)
(1): StackedConvLayers(
(blocks): Sequential(
(0): ConvDropoutNormNonlin(
(conv): Conv3d(32, 64, kernel_size=[1, 3, 3], stride=[1, 2, 2], padding=[0, 1, 1])
(instnorm): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)
(lrelu): LeakyReLU(negative_slope=0.01, inplace=True)
)
(1): ConvDropoutNormNonlin(
(conv): Conv3d(64, 64, kernel_size=[1, 3, 3], stride=(1, 1, 1), padding=[0, 1, 1])
(instnorm): InstanceNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)
(lrelu): LeakyReLU(negative_slope=0.01, inplace=True)
)
)
)
(2): StackedConvLayers(
(blocks): Sequential(
(0): ConvDropoutNormNonlin(
(conv): Conv3d(64, 128, kernel_size=[1, 3, 3], stride=[1, 2, 2], padding=[0, 1, 1])
(instnorm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)
(lrelu): LeakyReLU(negative_slope=0.01, inplace=True)
)
(1): ConvDropoutNormNonlin(
(conv): Conv3d(128, 128, kernel_size=[1, 3, 3], stride=(1, 1, 1), padding=[0, 1, 1])
(instnorm): InstanceNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)
(lrelu): LeakyReLU(negative_slope=0.01, inplace=True)
)
)
)
(3): StackedConvLayers(
(blocks): Sequential(
(0): ConvDropoutNormNonlin(
(conv): Conv3d(128, 256, kernel_size=[3, 3, 3], stride=[1, 2, 2], padding=[1, 1, 1])
(instnorm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)
(lrelu): LeakyReLU(negative_slope=0.01, inplace=True)
)
(1): ConvDropoutNormNonlin(
(conv): Conv3d(256, 256, kernel_size=[3, 3, 3], stride=(1, 1, 1), padding=[1, 1, 1])
(instnorm): InstanceNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)
(lrelu): LeakyReLU(negative_slope=0.01, inplace=True)
)
)
)
(4): StackedConvLayers(
(blocks): Sequential(
(0): ConvDropoutNormNonlin(
(conv): Conv3d(256, 320, kernel_size=[3, 3, 3], stride=[2, 2, 2], padding=[1, 1, 1])
(instnorm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)
(lrelu): LeakyReLU(negative_slope=0.01, inplace=True)
)
(1): ConvDropoutNormNonlin(
(conv): Conv3d(320, 320, kernel_size=[3, 3, 3], stride=(1, 1, 1), padding=[1, 1, 1])
(instnorm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)
(lrelu): LeakyReLU(negative_slope=0.01, inplace=True)
)
)
)
(5): StackedConvLayers(
(blocks): Sequential(
(0): ConvDropoutNormNonlin(
(conv): Conv3d(320, 320, kernel_size=[3, 3, 3], stride=[2, 2, 2], padding=[1, 1, 1])
(instnorm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)
(lrelu): LeakyReLU(negative_slope=0.01, inplace=True)
)
(1): ConvDropoutNormNonlin(
(conv): Conv3d(320, 320, kernel_size=[3, 3, 3], stride=(1, 1, 1), padding=[1, 1, 1])
(instnorm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)
(lrelu): LeakyReLU(negative_slope=0.01, inplace=True)
)
)
)
(6): Sequential(
(0): StackedConvLayers(
(blocks): Sequential(
(0): ConvDropoutNormNonlin(
(conv): Conv3d(320, 320, kernel_size=[3, 3, 3], stride=[1, 2, 2], padding=[1, 1, 1])
(instnorm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)
(lrelu): LeakyReLU(negative_slope=0.01, inplace=True)
)
)
)
(1): StackedConvLayers(
(blocks): Sequential(
(0): ConvDropoutNormNonlin(
(conv): Conv3d(320, 320, kernel_size=[3, 3, 3], stride=(1, 1, 1), padding=[1, 1, 1])
(instnorm): InstanceNorm3d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)
(lrelu): LeakyReLU(negative_slope=0.01, inplace=True)
)
)
)
)
)
(td): ModuleList()
(up0): ModuleList(
(0): ConvTranspose3d(320, 320, kernel_size=[1, 2, 2], stride=[1, 2, 2], bias=False)
(1): ConvTranspose3d(320, 320, kernel_size=[2, 2, 2], stride=[2, 2, 2], bias=False)
(2): ConvTranspose3d(320, 256, kernel_size=[2, 2, 2], stride=[2, 2, 2], bias=False)
(3): ConvTranspose3d(256, 128, kernel_size=[1, 2, 2], stride=[1, 2, 2], bias=False)
(4): ConvTranspose3d(128, 64, kernel_size=[1, 2, 2], stride=[1, 2, 2], bias=False)
(5): ConvTranspose3d(64, 32, kernel_size=[1, 2, 2], stride=[1, 2, 2], bias=False)
)
(up1): ModuleList(
(0): ConvTranspose3d(320, 320, kernel_size=[2, 2, 2], stride=[2, 2, 2], bias=False)
(1): ConvTranspose3d(320, 256, kernel_size=[2, 2, 2], stride=[2, 2, 2], bias=False)
(2): ConvTranspose3d(256, 128, kernel_size=[1, 2, 2], stride=[1, 2, 2], bias=False)
(3): ConvTranspose3d(128, 64, kernel_size=[1, 2, 2], stride=[1, 2, 2], bias=False)
(4): ConvTranspose3d(64, 32, kernel_size=[1, 2, 2], stride=[1, 2, 2], bias=False)
)
(up2): ModuleList(
(0): ConvTranspose3d(320, 256, kernel_size=[2, 2, 2], stride=[2, 2, 2], bias=False)
(1): ConvTranspose3d(256, 128, kernel_size=[1, 2, 2], stride=[1, 2, 2], bias=False)
(2): ConvTranspose3d(128, 64, kernel_size=[1, 2, 2], stride=[1, 2, 2], bias=False)
(3): ConvTranspose3d(64, 32, kernel_size=[1, 2, 2], stride=[1, 2, 2], bias=False)
)
(up3): ModuleList(
(0): ConvTranspose3d(256, 128, kernel_size=[1, 2, 2], stride=[1, 2, 2], bias=False)
(1): ConvTranspose3d(128, 64, kernel_size=[1, 2, 2], stride=[1, 2, 2], bias=False)
(2): ConvTranspose3d(64, 32, kernel_size=[1, 2, 2], stride=[1, 2, 2], bias=False)
)
(up4): ModuleList(
(0): ConvTranspose3d(128, 64, kernel_size=[1, 2, 2], stride=[1, 2, 2], bias=False)
(1): ConvTranspose3d(64, 32, kernel_size=[1, 2, 2], stride=[1, 2, 2], bias=False)
)
(seg_outputs): ModuleList(
(0): Conv3d(32, 2, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
(1): Conv3d(32, 2, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
(2): Conv3d(32, 2, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
(3): Conv3d(32, 2, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
(4): Conv3d(32, 2, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False)
)
)
14:21:08.713013:
14:21:08.713932:
epoch: 0
Traceback (most recent call last):
File "/python3.7.5/bin/nnUNet_train", line 11, in
load_entry_point('nnunet', 'console_scripts', 'nnUNet_train')()
File "/UnetPlusPlus/UNetPlusPlus-master/UNetPlusPlus-master/pytorch/nnunet/run/run_training.py", line 148, in main
trainer.run_training()
File "/UnetPlusPlus/UNetPlusPlus-master/UNetPlusPlus-master/pytorch/nnunet/training/network_training/nnUNetPlusPlusTrainerV2.py", line 424, in run_training
ret = super().run_training()
File "/UnetPlusPlus/UNetPlusPlus-master/UNetPlusPlus-master/pytorch/nnunet/training/network_training/nnUNetTrainer.py", line 316, in run_training
super(nnUNetTrainer, self).run_training()
File "/UnetPlusPlus/UNetPlusPlus-master/UNetPlusPlus-master/pytorch/nnunet/training/network_training/network_trainer.py", line 491, in run_training
l = self.run_iteration(self.tr_gen, True)
File "/UnetPlusPlus/UNetPlusPlus-master/UNetPlusPlus-master/pytorch/nnunet/training/network_training/nnUNetPlusPlusTrainerV2.py", line 242, in run_iteration
output = self.network(data)
File "/python3.7.5/lib/python3.7/site-packages/torch/nn/modules/module.py", line 722, in _call_impl
result = self.forward(*input, **kwargs)
File "/UnetPlusPlus/UNetPlusPlus-master/UNetPlusPlus-master/pytorch/nnunet/network_architecture/generic_UNetPlusPlus.py", line 409, in forward
x0_1 = self.loc4[0](torch.cat([x0_0, self.up40], 1))
File "/python3.7.5/lib/python3.7/site-packages/torch/nn/modules/module.py", line 722, in _call_impl
result = self.forward(*input, **kwargs)
File "/python3.7.5/lib/python3.7/site-packages/torch/nn/modules/conv.py", line 1066, in forward
output_padding, self.groups, self.dilation)
RuntimeError: Given transposed=1, weight of size [128, 64, 1, 2, 2], expected input[2, 64, 16, 160, 160] to have 128 channels, but got 64 channels instead
Exception in thread Thread-4:
Traceback (most recent call last):
File "/python3.7.5/lib/python3.7/threading.py", line 926, in _bootstrap_inner
self.run()
File "/python3.7.5/lib/python3.7/threading.py", line 870, in run
self._target(*self._args, **self._kwargs)
File "/UnetPlusPlus/UNetPlusPlus-master/UNetPlusPlus-master/pytorch/batchgenerators-master/batchgenerators/dataloading/multi_threaded_augmenter.py", line 99, in results_loop
raise RuntimeError("Someone died. Better end this madness. This is not the actual error message! Look "
RuntimeError: Someone died. Better end this madness. This is not the actual error message! Look further up your stdout to see what caused the error. Please also check whether your RAM was full
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